Browsing by Author "Doǧan, Refika Sultan"

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Hepatoselüler Karsinom Oluşumunda Etkili Moleküler Mekanizmaların İn Siliko Yöntemlerle Araştırılması
(Institute of Electrical and Electronics Engineers Inc., 2020) Doǧan, Refika Sultan; Saka, Samed; Bakir-Güngör, Burcu; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Hepatocellular carcinoma (HCC) is the most common cause of cancer-related death in the world. The molecular changes in the organism during the development of HCC are not fully understood. The aim of the present study is to contribute to the identification of critical genes and pathways associated with HCC via integrating various bioinformatics methods. In this study, experiments were conducted on gene expression data of 14 HCC tissues and noncancerous control tissues. A total of 1229 genes, which show a statistically significant change between the groups, were identified. Among these, 681 genes were upregulated and 548 genes were downregulated. Via mapping the detected genes into protein protein interaction networks, active subnetwork search, subnetwork topological analysis and functional enrichment analyses were carried out. The interactions between the molecular pathways affected by HCC were also presented. © 2020 Elsevier B.V., All rights reserved.
Citation - Scopus: 2
Hyperplastic and Tubular Polyp Classification Using Machine Learning and Feature Selection
(Elsevier B.V., 2024) Doǧan, Refika Sultan; Akay, Ebru; Doǧan, Serkan; Yilmaz, Bulent; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
Purpose: The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms. Methods: This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers. Results: The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower. Conclusion: This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology. © 2024 Elsevier B.V., All rights reserved.
Polyp Localization in Colonoscopy Images Using Vessel Density
(Institute of Electrical and Electronics Engineers Inc., 2018) Doǧan, Refika Sultan; Yilmaz, Bulent; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
In this paper, we present a new approach for polyp localization in colonoscopy images. This approach is based on the determination of the polyp location using the vessel density in colon images. Primarily, we used pre-processing procedures on the colon images, and then blood vessel extraction techniques were employed. Later, segmentation of the vessel boundaries was performed. With the help of vessel boundaries we calculated the vessel density, and used this for the localization of the polyps. We tested the success of this approach using a publicly available image set (CVC-ClinicDB database). This database consisted of 612 images from 29 different polyps. This approach succeeds in correct detection of 24 out of 29 different polyps. © 2019 Elsevier B.V., All rights reserved.